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16th International Conference on Advanced Technologies for Communications, ATC 2023

作者： Kojima, Toshiharu Iwabuchi, Koki The University of Electro-Communications Graduate School of Informatics and Engineering Department of Computer and Network Engineering 1-5-1 Chofugaoka Tokyo Chofu182-8585 Japan

ISBN: (纸本)9798350301328

This paper proposes a novel automatic frequency control (AFC) for modified Walsh-Hadamard code division multiplexing (MWHCDM). While MWHCDM is suitable for helicopter satellite communications, where rotor blades periodically block the transmission channel, it is affected by a time-varying Doppler shift due to helicopter maneuvers. The conventional AFC has a problem where its pull-in range is significantly narrower than the fluctuation range of the Doppler shift. The proposed AFC utilizes the structure of MWHCDM signals, and its operating range covers the fluctuation range with enough margin. computer simulation shows that the proposed AFC outperforms the conventional one on both operating/pull-in range and frequency estimation accuracy. © 2023 IEEE.

关键词： Frequency estimation

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Deep Learning Electric Load Forecasting for the Greek Power System 27

Deep Learning Electric Load Forecasting for the Greek Power ...

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27th International Conference on Circuits, Systems, Communications and computers, CSCC 2023

作者： Zelios, Vasileios Mastorocostas, Paris Kandilogiannakis, George Kesidis, Anastasios Tselenti, Panagiota Voulodimos, Athanasios University of West Attica Dept. of Informatics and Computer Engineering Athens Greece University of West Attica Dept. of Surveying and Geoinformatics Engineering Athens Greece School of Electrical and Computer Engineering National Technical University of Athens Athens Greece

ISBN: (纸本)9798350337594

Long Short-Term Memory and Gated Recurrent Unit-based networks are examined for short-term load forecasting for the Greek power system, where predictions of the next hour's load are made for the whole year. The recurrent property of the forecasters has the advantage to allow the use of a reduced input set since no prior knowledge of the most suitable previous loads is required for the model to identify the temporal relations that exist in the load time-series. An extensive simulation analysis is conducted, to evaluate the forecasters' performance and to highlight their particular learning characteristics. Both deep learning-based forecasters perform very efficiently, with LSTM achieving an average percentage error of 1.18% and a root mean squared error of 83.8 MW, while GRU performs similarly with respective values of 1.17% and 83.4 MW. © 2023 IEEE.

关键词： Long short-term memory

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Privacy-Preserving Collaborative AI for Distributed Deep Learning with Cross-sectional Data 5

Privacy-Preserving Collaborative AI for Distributed Deep Lea...

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5th International Conference on Bio-engineering for Smart Technologies, BioSMART 2023

作者： Iqbal, Saeed Qureshi, Adnan N. Aurangzeb, Khursheed Javeed, Khalid University of Central Punjab Faculty of Information Technology & Computer Science Department of Computer Science Lahore Pakistan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh Saudi Arabia University of Sharjah College of Computing and Informatics Department of Computer Engineering Sharjah United Arab Emirates

ISBN: (纸本)9798350338492

The article discusses the challenges of using deep learning in healthcare due to the lack of extensive medical datasets and concerns about confidentiality and privacy. The article then focuses on the analysis of skin lesion images, which are complex and difficult to classify. The article presents a unique CNN model (skin-net) using a progressively private federated learning architecture to potentially address these issues. Recent advancements in medical image analysis using convolutional neural networks and federated learning techniques have shown promising results (96.19 accuracy, 96.37 sensitivity and 96.53 specificity) in accurately classifying skin diseases while maintaining data privacy. Using these methods offers a potentially effective way to examine human skin while improving the model's generalization performance through image augmentation. © 2023 IEEE.

关键词： Deep learning

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Toward Utilizing Bidirectional Multi-head Attention Technique for Automatic Correction of Grammatical Errors

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2024年第11期51卷 1886-1897页

作者： Mahmoud, Zeinab Kryvinska, Natalia Abdalsalm, Mohammed Solyman, Aiman Alfatemi, Ali Musyafa, Ahmad School of Computer Science and Technology Wuhan University of Technology China Department of Information Management and Business Systems Comenius University Slovakia Department of Computer Science University of Milan Italy CIS Department Fordham University BronxNY10458 United States Department of Informatics Engineering Pamulang University South Tangerang15310 Indonesia

Automatic Grammar Error Correction (GEC) models identify and correct a wide range of grammatical errors. Various strategies have been proposed for GEC, with the Neural Machine Translation (NMT) approach being the most effective. However, NMT-based GEC models with encoder-decoder layers rely heavily on the highest layer, leading to potential inaccuracies. Additionally, during inference, exposure bias can cause the model to substitute previously targeted words with incorrect alternatives. Another challenge is data scarcity. This paper introduces a GEC model leveraging the seq-to-seq Transformer framework, specifically designed for low-resource languages like Arabic. We propose a method to generate noise in the text to create synthetic parallel data, addressing the data constraints. Inspired by Capsule Networks (CapsNet), we incorporate CapsNet in GEC to dynamically aggregate information from multiple layers. In order to mitigate exposure bias, we incorporated a bidirectional training approach and a regularization term using Kullback-Leibler divergence to align left-to-right and right-to-left models. Experiments on two benchmarks demonstrate that our model outperforms current Arabic GEC models, achieving the highest scores. The code is available on GitHub (https://***/Zainabobied/ArabicGEC). © (2024), (International Association of Engineers). All rights reserved.

关键词： Neural machine translation

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Development of Automotive-mobility Training in the TRIREME project 30

Development of Automotive-mobility Training in the TRIREME p...

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30th IEEE International Symposium for Design and Technology in Electronic Packaging, SIITME 2024

作者： Medgyes, Balint Farkas, Csaba Spanyik, Marek Danieli, Stefano Cantiani, Elena Stolfa, Jakub Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Electronics Technology Budapest Hungary VSB-Technical University of Ostrava Faculty of Electrical Engineering and Computer Science Department of Computer Science Ostrava Czech Republic Mylia-Adecco Formazione Milan Italy

ISBN: (纸本)9798331539511

To achieve climate neutrality by 2050, the consistent transformation of the mobility sector is one of the most important pillars of the European Green deal. As a result of green and sustainable approaches, novel automotive concepts and massive digitalisation in the mobility ecosystem is undergoing a continuous development towards eco-friendly solutions and novel business models, and the changing landscape of automotive industry comes with a significant social impact as well. The overall horizontal collaboration on skills agenda in the Automotive-Mobility Ecosystem is assured throughout the Automotive Skills Alliance (ASA) [1], which is the Large-scale Pact for Skills Partnership. The latest strategic project of the partnership to support its activities is the Digital & Green Skills Towards Future of the Mobility Ecosystem (TRIREME) which promotes a collaboration between industry stakeholders and training providers including Vocational education Trainings (VETs), Higher Educations (HEs) and universities), moreover, also emphasizing the widespread public representation of different project segments and evaluating the social impacts and regional impact of the changes in the Automotive-Mobility Ecosystem. In parallel to the development of sectoral skills strategy, our department (Department of Electronics Technology-ETT) is contributing to deliver specific training materials and courses within Work Package 4 (WP4), which is based on the gathered and processed intelligence from previous desk research, survey activity and participation in professional events. The methodology of course development relies on the concept of modularity, our professional competences in the field of Automotive-mobility, and the fulfillment of requirements to cover the needed skills, the competence and the occupational profile subsets. © 2024 IEEE.

关键词： Automotive Mobility Modular Courses Skills Training Development

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Estimating Uncertainty in Deep Learning Methods and Applications

Estimating Uncertainty in Deep Learning Methods and Applicat...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Alqarafi, Abdulrahman Batool, Hina Abbas, Tahir Janjua, Jamshaid Iqbal Ramay, Sadaqat Ali Ahmed, Munir College of Computer Science and Engineering Taibah University Medina42353 Saudi Arabia Times Institute Department of Computer Science Multan Pakistan Lahore Pakistan College of Informatics Korea University Seoul02841 Korea Republic of

ISBN: (纸本)9798350367560

Uncertainty estimation in deep learning has emerged as a crucial area of research due to its significance in enhancing model reliability and decision-making in critical applications. This article explores various methods and applications of uncertainty estimation in deep learning, aiming to provide insights into its importance, methods, and potential impact. Through a comprehensive literature review and analysis, the study identifies key findings regarding the effectiveness, limitations, and ethical considerations associated with uncertainty estimation techniques. The results reveal the diverse range of methodologies employed, including Bayesian approaches, ensemble methods, and Monte Carlo sampling, each with its strengths and drawbacks. Furthermore, the article discusses the implications of uncertainty estimation in deep learning for fields such as healthcare, autonomous systems, and safety-critical domains. Overall, this study underscores the significance of uncertainty estimation in deep learning and provides valuable insights for researchers and practitioners in the field. © 2024 IEEE.

关键词： Adversarial machine learning

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DeepFuzzLoc Positioning: A Unified Fusion of Fuzzy Clustering and Deep Learning for Scalable Indoor Localization Using Wi-Fi RSSI 13

DeepFuzzLoc Positioning: A Unified Fusion of Fuzzy Clusterin...

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Mahali, Muhammad Izzuddin Leu, Jenq-Shiou Achmad Sulistyo Putro, Nur Widyawan Prakosa, Setya Avian, Cries National Taiwan University of Science and Technology Dept. of Electronic and Computer Engineering Taipei Taiwan Universitas Negeri Yogyakarta Dept. of Electronics and Informatics Engineering Education Yogyakarta Indonesia Universitas Gadjah Mada Dept. of Computer Science and Electronics Yogyakarta Indonesia

ISBN: (纸本)9798350373332

This research introduces a novel method for indoor positioning by integrating Fuzzy C-Means clustering with deep learning autoencoders, leveraging Wi-Fi RSSI fingerprinting for precise location prediction and building and floor level classification within a unified framework. The approach combines the comprehensibility of Fuzzy C-Means (FCM) with the robust feature extraction capabilities of deep autoencoders through a dual-phase strategy: extensive offline training utilizing RSSI fingerprint data followed by real-time online position prediction. The model's performance was rigorously tested using 5-Fold cross-validation, examining its effectiveness both with and without the integration of FCM on various cluster numbers. Evaluation metrics, including Euclidean distance error for regression and accuracy, F1-score, precision, and specificity for classification effectiveness, were used for comparative analysis. The results were visualized through regression plots, confusion matrices, and learning curves, supplemented by t-SNE visualizations that provide profound insights into the feature space. These detailed analyses highlight the considerable promise of this integrated approach in enhancing the accuracy and reliability of indoor positioning systems. © 2024 IEEE.

关键词： Fuzzy clustering

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Ergodic Capacity Analysis of RIS-aided System Relying on User Grouping and Fixed Power Allocation 5th

Ergodic Capacity Analysis of RIS-aided System Relying on Use...

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5th International Conference on Communication, Device and Networking, ICCDN 2021

作者： Umamaheswari, Kaveti Fazal-E-Asim Do, Dinh-Thuan Department of Computer Science and Information Engineering College of Information and Electrical Engineering Asia University Taichung41354 Taiwan Department of Tele Informatics Engineering Federal University of Cear ´a Fortaleza Brazil

ISBN: (纸本)9789811920035

In this paper, we focus on the ergodic capacity of RIS-assisted NOMA in wireless communication systems. The closed upper bound of ergodic capacity analysis is obtained for the proposed technique by using both Rayleigh and Rician fading channel environments for multiple users. Compared to simulations, numerical results demonstrate the tightness and effectiveness of our closed-form expressions. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Rayleigh fading

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Performance Comparison of the Invasive Weed Optimization K-Means Algorithm With Particle Swarm Optimization K-Means for Banking Data 6

Performance Comparison of the Invasive Weed Optimization K-M...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Suwirmayanti, Ni Luh Gede Pivin Putra, I. Ketut Gede Darma Sudarma, Made Sukarsa, I. Made Setyaningsih, Emy Department of Computer Systems Faculty of Informatics and Computer Institut Teknologi dan Bisnis STIKOM Bali Bali Indonesia Department of Information Technology Faculty of Engineering Udayana University Jimbaran Hill Bali Indonesia Department of Electrical Engineering Faculty of Engineering Udayana University Jimbaran Hill Bali Indonesia Department of Computer Systems Engineering Faculty of Applied Science Universitas AKPRIND Indonesia Yogyakarta Indonesia

ISBN: (纸本)9798331539603

Banking produces extensive and diverse data, so a clustering process is needed to understand customer behavior patterns and transactions more effectively. This clustering has been widely utilized with the K-Means algorithm due to its simplicity and efficiency. However, this algorithm is limited in determining the optimal cluster center, affecting the accuracy of the clustering results. This study proposes applying two metaheuristic algorithms, Invasive Weed Optimization (IWO) and Particle Swarm Optimization (PSO), as additional solutions combined with K-Means. These two algorithms were chosen because of their ability to find global solutions and convergence speed to handle large and complex banking data. The results show the superiority of IWOKM compared to PSOKM in terms of SSE and DBI. From the SSE value, IWOKM produces a lower value (3029.77), which indicates that this method is better at minimizing clustering errors and producing more accurate clustering than PSOKM (80007.09). From the DBI value, IWOKM also produces a better value (1.6684) than PSOKM (1.6782), which shows that the clusters produced by IWOKM are more compact and more separated. However, in terms of computation time, PSOKM is more efficient, with a faster average computation time. Although IWOKM produces better clustering quality, PSOKM offers advantages in terms of processing speed. This finding confirms that the selection of the IWO algorithm is more appropriate for use on datasets with characteristics such as the German Credit Dataset. © 2024 IEEE.

关键词： Invasive weed optimization

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An automated speech analysis system for the detection of cognitive decline in elderly

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International Journal of Speech Technology 2023年第2期26卷 337-353页

作者： Loizou, Christos P. Pantzaris, Marios Faculty of Engineering & Technology Department of Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology 30 Arch. Kyprianos Str. Office 420 Limassol3036 Cyprus Cyprus Institute of Neurology and Genetics P. O. Box 23462 Nicosia1683 Cyprus

The goal of this study is to develop and test an automated integrated speech analysis system for detecting mild cognitive impairment (MCI) and dementia in spontaneous free speech. During the years 2010–2016, speech recordings (N = 2800) were obtained from 200 Greek Cypriots over the age of 65. These were divided into three groups (G1, G2, and G3) based on the results of their Mini-mental state examination (MMSE): G1:95 normal (NOR) individuals with an MMSE greater than 26;G2:65 MCI subjects with 20 ≤ MMSE ≤ 26;G3:40 dementia subjects with 0 ≤ MMSE © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Neurodegenerative diseases

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